IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v51y2024i3p610-624.html
   My bibliography  Save this article

A centrality measure for grid street network considering sequential route choice behaviour

Author

Listed:
  • Shota Tabata

Abstract

This study proposes a novel centrality measure for a grid network based on pedestrians’ sequential route choices, which we call sequential choice betweenness centrality (SCBC). Although conventional centralities are popular tools for urban network analysis, we must be aware of their meaning in the context of urban planning. This study reinterprets the centralities at the point of pedestrian flow. We then formulate the pedestrian flow distribution based on sequential route choice and develop the SCBC as a function of the probability of going straight at an intersection. The sensitivity analysis shows the probability of minimising the difference between the SCBC and existing centralities while revealing the numerical and spatial features of the SCBC. The more biased the grid proportion, the less similar the SCBC is to the existing ones. Moreover, the SCBC tends to be larger than conventional centralities around the corner nodes of the grid network. The probability parameterises the SCBC to go straight and is related to the pedestrian’s environmental cognition level. This parameterisation enabled us to adapt to the expected pedestrian attribution and perform an in-depth analysis of street networks.

Suggested Citation

  • Shota Tabata, 2024. "A centrality measure for grid street network considering sequential route choice behaviour," Environment and Planning B, , vol. 51(3), pages 610-624, March.
  • Handle: RePEc:sae:envirb:v:51:y:2024:i:3:p:610-624
    DOI: 10.1177/23998083231186750
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/23998083231186750
    Download Restriction: no

    File URL: https://libkey.io/10.1177/23998083231186750?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Agryzkov, Taras & Tortosa, Leandro & Vicent, Jose F., 2019. "A variant of the current flow betweenness centrality and its application in urban networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 600-615.
    2. Mahendra Piraveenan & Mikhail Prokopenko & Liaquat Hossain, 2013. "Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-14, January.
    3. Sevtsuk, Andres & Basu, Rounaq, 2022. "The role of turns in pedestrian route choice: A clarification," Journal of Transport Geography, Elsevier, vol. 102(C).
    4. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    5. Shatu, Farjana & Yigitcanlar, Tan & Bunker, Jonathan, 2019. "Shortest path distance vs. least directional change: Empirical testing of space syntax and geographic theories concerning pedestrian route choice behaviour," Journal of Transport Geography, Elsevier, vol. 74(C), pages 37-52.
    6. Alexander Hellervik & Leonard Nilsson & Claes Andersson, 2019. "Preferential centrality – A new measure unifying urban activity, attraction and accessibility," Environment and Planning B, , vol. 46(7), pages 1331-1346, September.
    7. Curado, Manuel & Tortosa, Leandro & Vicent, Jose F., 2021. "Identifying mobility patterns by means of centrality algorithms in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    8. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anita Mezzetti & Loic Mar'echal & Dimitri Percia David & William Lacube & S'ebastien Gillard & Michael Tsesmelis & Thomas Maillart & Alain Mermoud, 2022. "TechRank," Papers 2210.07824, arXiv.org.
    2. Víctor Martínez & Fernando Berzal & Juan-Carlos Cubero, 2019. "NOESIS: A Framework for Complex Network Data Analysis," Complexity, Hindawi, vol. 2019, pages 1-14, October.
    3. Basu, Rounaq & Sevtsuk, Andres, 2022. "How do street attributes affect willingness-to-walk? City-wide pedestrian route choice analysis using big data from Boston and San Francisco," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 1-19.
    4. Feng, Yuhao & Wu, Shufan & Wu, Peixin & Su, Shiliang & Weng, Min & Bian, Meng, 2018. "Spatiotemporal characterization of megaregional poly-centrality: Evidence for new urban hypotheses and implications for polycentric policies," Land Use Policy, Elsevier, vol. 77(C), pages 712-731.
    5. Thomas J. Sargent & John Stachurski, 2022. "Economic Networks: Theory and Computation," Papers 2203.11972, arXiv.org, revised Jul 2022.
    6. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).
    7. D’Errico, Marco & Battiston, Stefano & Peltonen, Tuomas & Scheicher, Martin, 2018. "How does risk flow in the credit default swap market?," Journal of Financial Stability, Elsevier, vol. 35(C), pages 53-74.
    8. Liu, Xiaodong & Patacchini, Eleonora & Zenou, Yves & Lee, Lung-Fei, 2011. "Criminal Networks: Who is the Key Player?," Research Papers in Economics 2011:7, Stockholm University, Department of Economics.
    9. Agnieszka Rusinowska & Rudolf Berghammer & Harrie de Swart & Michel Grabisch, 2011. "Social networks: Prestige, centrality, and influence (Invited paper)," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00633859, HAL.
    10. Gabrielle Demange, 2018. "Contagion in Financial Networks: A Threat Index," Management Science, INFORMS, vol. 64(2), pages 955-970, February.
    11. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    12. Yao Hongxing & Lu Yunxia, 2017. "Analyzing the Potential Influence of Shanghai Stock Market Based on Link Prediction Method," Journal of Systems Science and Information, De Gruyter, vol. 5(5), pages 446-461, October.
    13. Zhepeng Li & Xiao Fang & Xue Bai & Olivia R. Liu Sheng, 2017. "Utility-Based Link Recommendation for Online Social Networks," Management Science, INFORMS, vol. 63(6), pages 1938-1952, June.
    14. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Shokrollahi, Arman, 2015. "Improving detection of influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 833-845.
    15. Dequiedt, Vianney & Zenou, Yves, 2017. "Local and consistent centrality measures in parameterized networks," Mathematical Social Sciences, Elsevier, vol. 88(C), pages 28-36.
    16. ,, 2014. "A ranking method based on handicaps," Theoretical Economics, Econometric Society, vol. 9(3), September.
    17. Ernest Liu & Aleh Tsyvinski, 2021. "Dynamical Structure and Spectral Properties of Input-Output Networks," Working Papers 2021-13, Princeton University. Economics Department..
    18. Richard W. Carney & Travers Barclay Child, 2015. "Business Networks and Crisis Performance: Professional, Political, and Family Ties," Tinbergen Institute Discussion Papers 15-135/V, Tinbergen Institute, revised 20 Feb 2015.
    19. Wu, Tao & Xian, Xingping & Zhong, Linfeng & Xiong, Xi & Stanley, H. Eugene, 2018. "Power iteration ranking via hybrid diffusion for vital nodes identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 802-815.
    20. Shenshen Bai & Longjie Li & Jianjun Cheng & Shijin Xu & Xiaoyun Chen, 2018. "Predicting Missing Links Based on a New Triangle Structure," Complexity, Hindawi, vol. 2018, pages 1-11, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:51:y:2024:i:3:p:610-624. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.